Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.
Nuisance function tuning and sample splitting for optimal doubly robust estimation
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
DML estimators for the quadratic functional and quadratic density integral are asymptotically inadmissible under SA models and dominated by empirical HOIF estimators, while DML remains minimax for expected conditional covariance.
Develops higher-order influence function estimators for implicitly defined parameters in non-separable structural models using U-processes theory.
citing papers explorer
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Private Rate-Double-Robust Inference
Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.
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On the Asymptotic Inadmissibility of Double Machine Learning Estimators Under Structure-Agnostic Models
DML estimators for the quadratic functional and quadratic density integral are asymptotically inadmissible under SA models and dominated by empirical HOIF estimators, while DML remains minimax for expected conditional covariance.